Phishing attacks are among the most prevalent forms of cybercrime, exploiting human vulnerabilities to deceive individuals and organizations. This research seeks to explore the human element in phishing attacks to develop more effective prevention strategies. Phishing's success is deeply rooted in psychological and behavioral factors, which influence an individual’s susceptibility to these attacks. Our study aims to identify these key psychological traits and behaviors that heighten vulnerability, such as cognitive biases, trust in online communications, and lack of cybersecurity awareness.

In addition to this exploration of human factors, we will conduct a comprehensive literature review of the latest Machine Learning (ML) techniques used to detect and prevent phishing attacks, examining their strengths, limitations, and real-world applicability. To deepen the understanding of human vulnerabilities in phishing, we will also gather data through surveys, interviews, and Open-Source Intelligence (OSINT), ensuring a diverse and multidimensional perspective on the issue. By analyzing this data, we aim to bridge gaps in current knowledge and provide new insights into how human behavior can be better addressed in phishing prevention strategies.

Furthermore, the study will explore potential areas for improvement in existing mitigation methods, considering both technological and behavioral approaches. The findings will contribute to the body of knowledge by offering practical, data-driven recommendations for designing more targeted and effective countermeasures to protect individuals and organizations from phishing attacks. Ultimately, this research aims to provide a foundation for future developments in both human-centered and Machine Learning-based phishing defense strategies.